Why do layered neural nets struggle with continous data?
They struggle because if your network have an inductive bias towards modeling datasets which are described with ODEs well, you will learn faster, and with smaller dataset. I think, this what the authors of the original article meant.

In a similar way, CNNs recognize images much better, because their features are translation invariant whereas fully connected net needs to learn to recognize a cat in each different position from scratch.